D2.1 Requirements and Specification - CORBYS

D2.1 Requirements and Specification
stimulus was provided indicating the accuracy of the previous estimation. Each trial starts with a warning cue.
Brain activity of five healthy subjects was measured using 32 electrodes placed at FP1, FP2, F7, F8, F3, F4,
T7, T8, C3, C4, P7, P8, P3, P4, O1, O2, AF3, AF4, FC5, FC6, FC1, FC2, CP5, CP6, CP1, CP2, Fz, FCz, Cz,
CPz, Pz and Oz. The signals were classified employing a Support Vector Machines (SVM) with radial basis
function. This study analysed the requirements of the classifier in terms of the amount of training data, its
performance among sessions and the possibility of fast re-training in order to achieve good performances
using data from previous sessions. Also an online analysis of the data was performed showing an average
recognition rate of (78%). CORBYS will progress the state-of-the-art by designing a real-time detection
system of feedback errors in robotic applications, analysing the different feedback modalities (e.g. visual,
auditory, vibrotactile, etc) that best suit for the robotic rehabilitation scenario defined.
15.5.3 EEG Decoding of Attentional States
Cognitive processes are produced and controlled within the central nervous system (CNS), accordingly brain
and physiological activity of the body reflect these states. Cognitive states change the patterns of
physiological signals (e.g. heart rate, skin temperature, respiration, etc.), several biosensors have been used to
identify them; stress, relaxation and exhaustion conditions were analysed the most often (Shi et al, 2007; Zhai
& Barreto, 2006; Kulic & Croft, 2007). Over the last few decades several studies have put in evidence the
relation between attention, or other relevant mental conditions, and EEG spectral features. For instance, in
Jung et al, (1997) a power spectrum estimation was combined with principal component analysis (PCA) and
artificial neural networks to estimate a local error rate in a sustained attention task. Others, instead, have
focused on specific EEG rhythms. In Kelly et al, (2003) and Huang et al, (2007) alpha band power was
examined to investigate the attentional demands and the brain dynamics following vehicle deviation in
sustained attention tasks, respectively. In addition alpha, the gamma band, with frequencies greater than 30
Hz, was analysed to determine its enhancement during a visual spatial attention task (i.e. moving-bar-like
paradigm) (Gruber et all, 1999). In Haufler et al, (2000), log-transformed EEG power spectral estimates for
various frequency bands were compared during a selfpaced visuospatial task from skilled marksmen and
novice gunmen. Beyond attention, increased in alpha (Foster, 1990; Lindsley, 1952; Brown, 1970) and theta
powers have been interpreted as a signs of relaxation (Teplan et al, 2009), in Teplan et al, (2006) this was
shown during long term audio-visual stimulation. Furthermore, related to attention, clinical studies were
conduced on Attention Deficit Hyperactivity Disorder (ADHD), suggesting that theta/beta self-regulation
reduces its symptoms (Barry et al, 2003; Monastra et al, 2005), quantifying the deficit (Clarke et al, 2001;
Koehler et al, 2009) and represents the basis of the neurofeedback treatment (Lubar, 1991; Linden et al, 1996;
Friel, 2007). Recently, there is evidence that the states of attention and non attention can be discriminated
achieving up to 89% classification accuracy rate in an online environment using a novel approach to extract,
select and learn EEG spectral-spatial patterns. This new approach combines advanced signal processing and
machine learning: the filtering pre-processing step consists of two stage, a filter-bank (FB) and common
spatial patterns (CSP) filters, while a mutual information technique selecting best features with a linear
classifier were applied to measure the attention level (Hamadicharef et al, 2009). Aside from visual attention,
attentional modulation of auditory event-related potentials was reported. Listening to two concurrent auditory
stimuli, the event-related EEG is modulated by the user selective attention to one or the other (Hillyard et al,
1973; Ntnen, 1982, 1990). These results were exploited to develop a BCI paradigm, in which the subject
could make a binary choice by focusing its attention (Hill et al, 2004).
Technological Gaps and related CORBYS innovation: During motor execution, attention to movement
task related features plays a fundamental role affecting motor performance (Ingram et al, 200; Zachry et al,
2005). Furthermore, several studies have investigated in the attention function during the learning process of
novel sensorimotor transformation and the adaptation process to novel force perturbations, reporting its
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